Affine correspondences are commonly estimated using either frame-to-frame tracking techniques [1]}, [2]} or by affine-covariant region detection [3]}. In the case of frame-to-frame tracking, the deformation of a locally planar surface observed by a moving camera is well-modeled by an affine transformation. For affine-covariant regions, an affine transformation describing the shape of the local region around the interest point is estimated [4]}. The affine transformation between two regions \(\mathsf {A}_i,\mathsf {A}_j\)  is then found by \(\mathsf {A}_j\mathsf {A}_i^{-1}\) . Although affine-covariant regions have been shown to improve match reliability in wide-baseline image matching [5]}, they also are known to be less robust to illumination change [6]}. The affine-covariant fitting step also increases the computation time required for feature detection. Recent work explores the application of convolutional neural networks to learn how to more reliably and quickly detect high-quality affine-covariant regions [7]}.
